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存储流动问题的理解机制探究
其他题名Exbloring the Mechanism of Understanding Stock-Flow Problems
乔韩
2019-06
摘要

存储流动问题是指:己知某系统各时刻的流入与流出值(变化量),求解系统各时刻累积量的问题。既往研究表明,大量受过高等教育的人也较难正确解答存储流动问题。人们普遍采用相关启发式,即认为最大流入或最大净流入为最大存量,最大流出或最大净流出为最小存量。

本研究从两个角度探究存储流动问题的理解机制。实验1-3从内容角度探究领域特异性对存储流动问题理解的影响。实验1通过图形表征的方式呈现存储流动问题,考察增加谎言含义是否能促进对存储流动问题的理解。发现欺骗有无在贩灾任务最大存储问题上达到边缘显著,在其他任务上并不显著。实验2通过表格表征的方式呈现存储流动问题,发现欺骗含义不能促进对存储流动问题的理解。实验3通过图形表征的方式呈现存储流动问题,检验危险含义对存储流动问题理解的促进作用,发现危险含义也不能促进对存储流动问题的理解。

实验4-6从视觉呈现角度探究视觉凸显对存储流动问题理解的影响。实验4通过图形表征的方式呈现存储流动问题(商店问题),检验高亮显示曲线间面积的视觉凸显方式对存储流动问题理解的促进作用,发现该方式可以显著促进对最小存储问题的理解,对最大存储问题理解的促进也达到了边缘显著。实验_5使用更多的任务进一步探究了实验4结论的可推广性。结果发现该呈现方式在多个任务中都有效果。实验6通过表格表征的方式呈现存储流动问题,检验备注净流入符号的视觉凸显方式对存储流动问题理解的促进作用,发现该方式不能促进对存储流动问题的理解。

本研究探究了领域特异性和视觉凸显对存储流动问题理解的促进作用。我们首先发现增加谎言和危险含义无法有效促进这一理解。对于前者,可能引发了被试的愤怒情绪导致更多地使用启发式思维和错误;对于后者,可能是因为被试对于“危险”的理解存在争议。此外,我们发现了高亮显示曲线间面积的视觉凸显方式可以促进对存储流动问题的理解,这一结果说明曲线间面积是能够促进对该问题理解的关键区域。从应用上来讲,我们的发现可以为增强大众理解存储流动问题提供一些参考。

其他摘要

Stock-flow problems refer to the understanding how the quantity of things in a given system changes due to inflow and outflow fluctuations. Studies have shown that even people with high education have obstacles in understanding stock-flow problems. People often confuse the maximum inflow/net inflow with the maximum stock, or confuse the minimum outflow/net outflow with the minimum stock, a phenomena termed correlation heuristic error.

This research explored the mechanism of understanding stock-flow problems from two perspectives. Experiment 1-3 investigated if stock-flow inference can be enhanced in some specific domain by providing certain context information. Experiment 1 examined whether adding cheating-related contents can promote the comprehension of stock-flow problems using a graphic presentation. Results showed that adding cheating-related contents only enhanced the understanding of the maximum stock problem in one of all three tasks. Experiment 2 presented stock-flow problems through tabular representation, but no effect of cheating was found. Experiment 3 investigated whether adding danger-related contexts can aid the understanding of stock-flow problems in a graphic presentation, but there was no effect of danger. By following the perspective that providing a visual presentation investigated whether providing salient visual cues can have a positive effect. Experiment 4 tests if highlighting the areas between the inflow and outflow curves can be helpful and results showed this method was effective. Experiment 5 explored whether the findings of experiment 4 could be generalized to other contexts, and the results indicated this approach had universal effects in solving maximum stock problems across three different tasks, and also increased the understanding of minimum stock problem in a finance task. Experiment 6 tested whether providing plus-minus signs in tables, another kind of visual cues to enhance the net inflow/outflow difference, could improve the understanding of stock-flow problems. Results indicated this method failed to increase the accuracy of stock-flow problems.

The research investigated whether adding some domain-specific contents and providing salient visual cues could facilitate one's comprehension of stock-flow problems. First, we found adding cheating-related contents and danger-related contents failed to promote understanding stock-flow problems. For the former, induced anger by cheating may result in more heuristic processing and errors; for the latter, inconsistent comprehension of danger may weaken the danger promotion effect. Secondly, we proved highlighting the areas between flow curves was an effective approach to promoting understanding stock-flow problems, which indicated the areas are the crucial parts for understanding stock-flow problems. In application, our findings can offer some basic references for enhancing public's comprehension of stock-flow problems.

关键词存储流动问题 领域特异性 视觉凸显
学位类型硕士
语种中文
学位名称理学硕士
学位专业应用心理学
学位授予单位中国科学院大学
学位授予地点中国科学院心理研究所
文献类型学位论文
条目标识符http://ir.psych.ac.cn/handle/311026/29293
专题社会与工程心理学研究室
推荐引用方式
GB/T 7714
乔韩. 存储流动问题的理解机制探究[D]. 中国科学院心理研究所. 中国科学院大学,2019.
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